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---
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language:
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- mn
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license: mit
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tags:
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- generated_from_trainer
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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model-index:
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- name: mongolian-xlm-roberta-base-demo
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# mongolian-xlm-roberta-base-demo
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1177
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- Precision: 0.9262
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- Recall: 0.9332
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- F1: 0.9297
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- Accuracy: 0.9785
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 16
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- eval_batch_size: 32
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 10
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 0.1979 | 1.0 | 477 | 0.1015 | 0.8713 | 0.8958 | 0.8834 | 0.9692 |
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| 0.0839 | 2.0 | 954 | 0.0965 | 0.9050 | 0.9125 | 0.9088 | 0.9743 |
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| 0.0604 | 3.0 | 1431 | 0.0844 | 0.9217 | 0.9258 | 0.9237 | 0.9771 |
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| 0.0455 | 4.0 | 1908 | 0.0955 | 0.9154 | 0.9283 | 0.9218 | 0.9774 |
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| 0.0337 | 5.0 | 2385 | 0.0923 | 0.9228 | 0.9318 | 0.9273 | 0.9787 |
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| 0.0254 | 6.0 | 2862 | 0.1055 | 0.9213 | 0.9303 | 0.9258 | 0.9776 |
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| 0.02 | 7.0 | 3339 | 0.1075 | 0.9244 | 0.9329 | 0.9286 | 0.9785 |
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| 0.0149 | 8.0 | 3816 | 0.1142 | 0.9262 | 0.9329 | 0.9295 | 0.9788 |
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| 0.0126 | 9.0 | 4293 | 0.1149 | 0.9219 | 0.9306 | 0.9262 | 0.9780 |
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| 0.01 | 10.0 | 4770 | 0.1177 | 0.9262 | 0.9332 | 0.9297 | 0.9785 |
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### Framework versions
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- Transformers 4.29.2
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- Pytorch 2.0.1+cu118
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- Datasets 2.12.0
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- Tokenizers 0.13.3
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